Study on Thin-Film Transistor and Circuit Design Using Artificial Intelligence
PDF
PDF

How to Cite

S., Manoharan. 2026. “Study on Thin-Film Transistor and Circuit Design Using Artificial Intelligence”. Journal of Electronics and Informatics 7 (4): 298-315. https://doi.org/10.36548/jei.2025.4.004.

Keywords

— Thin-Film Transistor (TFT)
— Artificial Intelligence
— Liquid Crystal Displays (LCDs)
— Integrated Circuits (IC’s)
Published: 10-01-2026

Abstract

This paper reviews the detailed inspection of the current situation of thin-film transistors (TFT) and circuit design using AI, its widespread applications, and the future changes that are about to take place in this field. In liquid crystal display (LCD) systems, thin-film transistors have bridged the gap between the technologies used in this application and have been employed in a wide variety of areas, such as flexible electronics, biological sensing, and integrated IoT platforms. The focus of the paper is on the areas where TFT is heading rather than those it has already traversed. A close look at the sectors reveals that high-resolution displays, where TFT is integrated, result from some vibrant circuits made on flexible substrates, which would not be possible without TFT. Artificial intelligence can be applied to predict and enhance manufacturing efficiency and to estimate costs more accurately. Integrating TFT thin-film transistors with AI integrated circuits in future electronic devices will represent a significant advancement in the field. The AI built-in circuit type augmented by thin-film transistors would unlock visionary progress in entrenched high-tech areas and provide a unique and innovative gateway for businesses and researchers.

References

  1. S. Balaji, "Digital Design Flow Techniques and Circuit Design for Thin-Film Transistors," M.S. thesis, Dept. Elect. Inf. Technol., Lund Univ., Lund, Sweden, 2020.
  2. S. Jiang, C. Li, J. Du, D. Wang, H. Ma, J. Yu, and A. Nathan, "Thin-Film Transistor Digital Microfluidics Circuit Design with Capacitance-Based Droplet Sensing," Sensors, vol. 24, no. 15, Art. no. 4789, Jul. 2024, doi: 10.3390/s24154789. sensors-24-04789.
  3. Y. Zhou and C. Dong, "Influence of Passivation Layers on Positive Gate Bias-Stress Stability of Amorphous InGaZnO Thin-Film Transistors," Micromachines, vol. 9, no. 11, Art. no. 603, Nov. 2018, doi: 10.3390/mi9110603.
  4. A. Van Calster, "Thin Film Transistors and Thin Film Transistor Circuits," Electrocomponent Science and Technology, vol. 10, 1983. 731689, 185–189.
  5. P. Xu and I. Shin, "Preparation and Performance Analysis of Thin-Film Artificial Intelligence Transistors Based on Integration of Storage and Computing," IEEE Access, vol. XX, 2024, 1–XX. doi: 10.1109/ACCESS.2024.3369171.
  6. A. Tixier-Mita, S. Ihida, B.-D. Ségard, G. A. Cathcart, T. Takahashi, H. Fujita, and H. Toshiyoshi, "Review on Thin-Film Transistor Technology, Its Applications, and Possible New Applications to Biological Cells," Jpn. J. Appl. Phys., vol. 55, no. 4, Art. no. 04EA08, Mar. 2016, doi: 10.7567/JJAP.55.04EA08.
  7. Y. Kuo, "Thin Film Transistor Technology—Past, Present, and Future," Electrochem. Soc. Interface, vol. 22, no. 1, Spring 2013, 55–63. doi: 10.1149/2.F06131if.
  8. S. Jeon and R. Dhivya, "Implementations of Artificial Intelligence for Automated Electrical Design in Integrated Circuits," Int. Innov. Res. J. Eng. Technol., vol. 8, no. 2, Dec. 2022, 20–28. doi: 10.32595/iirjet.org/v8i2.2022.166.
  9. J. Pan et al., "Transfer Learning-Based Artificial Intelligence-Integrated Physical Modeling to Enable Failure Analysis for 3 Nanometer and Smaller Silicon-Based CMOS Transistors," ACS Appl. Nano Mater., vol. 4, no. 7, Jun. 2021, 6903–6915. doi: 10.1021/acsanm.1c00960.
  10. S. Kim and H. Yoo, "Recent Progress in Thin-Film Transistors Toward Digital, Analog, And Functional Circuits," Micromachines, vol. 13, no. 12, Art. no. 2258, Dec. 2022, doi: 10.3390/mi13122258.
  11. J. Jang and S. Lee, "A Fundamental Reason for the Need of Two Different Semiconductor Technologies for Complementary Thin-Film Transistor Operations," Crystals, vol. 9, no. 11, Art. no. 603, Nov. 2019, doi: 10.3390/cryst9110603. crystals-09-00603-v2.
  12. N. Lu, W. Jiang, Q. Wu, D. Geng, L. Li, and M. Liu, "A Review for Compact Model of Thin-Film Transistors (TFTs)," Micromachines, vol. 9, no. 11, Art. no. 599, Nov. 2018, doi: 10.3390/mi9110599.